Search engines learning to anticipate user's needs

Artificial intelligence

Published 4:00 am, Monday, October 18, 2010

Photo: Liz Hafalia, The Chronicle

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With the help of scientist Amit Singhal, Google is creating algorithms that increasingly understand the actual intent in user searches to even predict the information and services people will want before they've articulated it in Mountain View, Calif., on Wednesday, October 13, 2010. less

With the help of scientist Amit Singhal, Google is creating algorithms that increasingly understand the actual intent in user searches to even predict the information and services people will want before ... more

Photo: Liz Hafalia, The Chronicle

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With the help of scientist Amit Singhal, Google is creating algorithms that increasingly understand the actual intent in user searches to even predict the information and services people will want before they've articulated it in Mountain View, Calif., on Wednesday, October 13, 2010. less

With the help of scientist Amit Singhal, Google is creating algorithms that increasingly understand the actual intent in user searches to even predict the information and services people will want before ... more

Photo: Liz Hafalia, The Chronicle

Search engines learning to anticipate user's needs

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The next time you get a helpful search result from Google, you might offer a nod of gratitude to a depressive Austrian philosopher named Ludwig Wittgenstein.

In a book published posthumously in 1953, he argued that words are so pliable as to lose most meaning outside of a sentence. The word "hit," for example, doesn't conjure up a distinct mental image until neighboring words like "movie," "baseball" or "the hay" tug your thoughts in the right direction.

Google Inc.Fellow Amit Singhal studied Wittgenstein while working on his doctorate in computer science at Cornell University, and integrated that idea into a major reworking of the company's core search algorithm unveiled in 2003.

It wasn't enough, he concluded, for Google to match up keywords in queries to the pages on which they're found online, because those words mean little in isolation. To provide truly relevant results, the Internet giant's machines had to begin to understand language the way that humans do.

"That basic, key insight was the driving force behind the last seven years of work that we've done, which has gone way beyond anything I have ever seen in the academic world," Singhal said.

To accomplish this, Google employed machine learning algorithms, essentially artificial intelligence programs that study patterns in the vast quantities of human writing across the Internet, to figure out the signals that suggest specific meanings of words.

Other companies and research institutes, including Microsoft Corp., SRI International, Wolfram|Alpha and Yahoo Inc., have applied similar methods to the problem of online search. As machines have become better at understanding the mechanics of human language, the quality of what we think of as search has vastly improved, while the very notion of what it means has expanded.

In fact, some believe computers will soon be capable of addressing our needs and wants before we've articulated them in our own minds.

The term online search suggests that people are just looking for information, but in many cases they're hoping to complete tasks.

For instance, if a user types or says "Oakland restaurant" in a search engine, often what they want to do is make reservations. But search-engine results typically start with a list of possible establishments. The user can then scroll through those, hop over to Yelp for reviews, and click onto OpenTable to see if they can make reservations.

Siri, an artificial intelligence iPhone app developed by SRI and later bought by Apple Inc., leapfrogs some of these steps. When a user says or types "Oakland restaurant," a list of spots pops up with ratings, prices and buttons that say "reserve table."

Over time, as the tool learns more about you - your location, preferences and habits - it can provide increasingly personalized results. It might know, for instance, that you favor fancy restaurants, French bistros or Korean BBQ.

Siri's founder declined an interview request, but SRI continues to work on virtual personal assistants that address this shortcoming of search engines.

"With most of the systems right now, we express it piece by piece. It's up to us, in our head, to make sure the pieces all flow together and at the end combine to meet our intent," said Bill Mark, vice president of information and computing sciences at SRI. "We're moving to a world where the technology does a better job of understanding higher level intent and completes the entire task for us."

Similarly, a search for "flights to Boston" on Microsoft's Bing search engine returns not just links to travel sites, but a box on the results page that allows users to enter flight dates and the departure airport to directly pull up times and prices. Bing also moves its local, video or map results to more or less prominent parts of the page, depending on how a user worded a query.

In both cases, the algorithm is making decisions based on its improving understanding of how humans use language.

Bing "is taking things to another level in terms of anticipating what users really want," said Jan Pedersen, chief scientist for core search at Microsoft. "There's a lot of ambiguity in the queries, but there's usually a very particular meaning that any particular user has."

'Autonomous search'

But such a leap might occur even without parceling out queries, if machines can learn to anticipate our desires based on other facts they collect.

As smart phones become increasingly ubiquitous, more and more of us are spending our days tethered to constantly connected gadgets that know: where we are at any given moment, the entries in our calendars and to-do lists, the albums we listen to the most, the headlines we read, who our friends are and where we shop.

There's an enormous amount of predictive power in all that data, if companies can connect even parts of it together. And it can be employed in a lot of different ways.

Singhal said it could help effectively schedule time. For instance, a smart phone could let users know when there's a store nearby where they can check off a task, if their calendar shows they have the time. The device would react to shifting circumstances - location and time, as well as entries on a calendar or to-do list - rather than a typed search query.

Google Chief Executive Officer Eric Schmidt described a similar scenario last month at a conference in Berlin, in which a history buff like himself automatically receives information from his handset about exhibitions at the museums he walked past in Germany.

He dubbed the concept "autonomous search."

"It knows who I am, it knows what I care about and it knows roughly where I am," he said of the smart phone. "The ability to tell me things I didn't know but I probably am very interested in is the next great step, in my view, of search."

He and Singhal both stressed that Google would ask permission from users before switching on such applications.

Seductive systems

But others are dubious of - or frightened by - the possibility.

Jeff Chester, executive director of the Center for Digital Democracy, said it's more akin to the next great step in targeted marketing. He believes that new rules must be established that take into account the accelerating collection and prediction abilities of these technologies.

At a minimum, privacy advocates say, consumers should have to opt in before any information can be collected about them and must be made aware of how such personal data might be used - standards that haven't been put in place for such information to date.

"There needs to be a very serious public debate about what the proper role and safeguards should be for the emergence of this ubiquitous, artificial intelligence-based world," Chester said.

"These are very powerful and seductive systems," he added. "But you're trading off your autonomy and ultimately your security for the ease of allowing someone else to make decisions for you and set your agenda."

Ray Valdes, an analyst with Gartner Research, said autonomous search remains a fairly far out technological possibility at this point.

He also stressed that the advances in online search realized from machine learning have been tempered by the increasing complexity of search.

The amount of online content has exploded, and consumer expectations have soared. They want to find pictures, video and music that often aren't accurately tagged with language. They want to pull relevant information from social sites like Facebook and Twitter. And they want to know what's on a page even if it's written in French or Spanish.

"Search engines have gotten better, but at the same time their tasks have gotten more difficult," he said.

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